Unmanned aerial vehicle (“UAV”) technology has proven to be a valuable tool for mission profiles involving intelligence, surveillance, reconnaissance, and payload delivery. In contexts such as low-altitude urban reconnaissance, a UAV, such as a micro-air vehicle (“MAV”) or unmanned aerial system (“UAS”), may encounter both large and small obstacles. To mitigate collision, the UAV may be localized to a particular location. More recently, UAVs have been employed to monitor the integrity of structure (e.g., buildings, bridges, etc.). As can be appreciated, scanning a structure requires both localization of the aircraft and techniques for registering, or stitches together, multiple point clouds generated by the UAV's scanner/cameras during a scanning process. Existing methods of localizing, or otherwise restricting, a UAS with respect to a single point on the ground rely on uncertain visual data sources, such as photogrammetry or visual odometry. The uncertainty introduced in using visual methods propagates with respect to the distance from the source and the errors are measured in pixels. Accordingly, a need exists for a system and method for localizing an aerial vehicle.